Hyperspectral imaging involves imaging multiple (e.g., dozens or hundreds of) narrow spectral bands over a spectral range to produce a composite image wherein each pixel in the scene being captured includes contiguous spectral data of the scene. An aspect of hyperspectral imaging combines a conventional two-dimensional (2D) spatial image with a third dimension of contiguous spectral bands, essentially performing spectrometry on each individual pixel of the 2D spatial image.
Conventional hyperspectral imaging involves generating a hyper-cube of data for the scene being imaged. A hyper-cube is in essence a three-dimensional (3D) image, where two of the dimensions are spatial and one dimension is contiguous spectral data. Depending on the device used to generate the data, the acquisition time for a single hyper-cube image from most hyperspectral systems can be on the order of tens of seconds before useful context-sensitive information can be extracted.
Due to the 3D nature of the data, hyper-cubes can be quite large, with their size depending on the spatial and spectral resolution of the image. While this amount of data collection is necessary for many hyperspectral applications, it is not necessary for all of them. In spectral detection applications, more often than not, the vast majority of the data collected is not needed.
One type of hyperspectral imaging system looks for specific pre-determined spectral signatures in a given area and is called a hyperspectral detector or HSD. In essence, HSDs are “Go/No-Go” sensors that verify the presence or absence of the particular spectral signatures that cannot be readily detected by visual methods or other means. It is often preferred that the information from the HSD be available in real-time so that users can take action in real-time rather than having to wait for the computation to be completed. Furthermore, for mobile or hand-held HSDs, computing power of the HSD may be limited.